Sift — banking77 intent classifier (modernbert)
Fine-tuned answerdotai/ModernBERT-base on PolyAI/banking77
(77 fine-grained banking intents). Part of Sift, a query-triage project
showing a small fine-tuned model beats a zero-shot open 7B instruct LLM
(Mistral-7B-Instruct, run locally and 4-bit quantised) on cost, latency, and
privacy for narrow, high-volume classification.
Metrics — full test split (3076 examples)
| Metric | Score |
|---|---|
| Accuracy | 0.9353 |
| Macro-F1 | 0.9353 |
| Median single-query latency | 22.47 ms (Tesla T4) |
Intent -> team routing
The 77 intents are mapped to six routing teams: Cards, Payments, Accounts, Fraud/Security, Onboarding, General.
Usage
from transformers import pipeline
clf = pipeline("text-classification", model="DarioFrK/sift-banking77-modernbert", top_k=5)
clf("My card was swallowed by the ATM")
- Base model:
answerdotai/ModernBERT-base - Dataset:
PolyAI/banking77 - Training: 3 epochs, lr=5e-05, batch=16, max_len=64